RT Journal Article
SR Electronic
T1 Beyond traditional visual sleep scoring: massive feature extraction and unsupervised clustering of sleep time series
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.09.08.458981
DO 10.1101/2021.09.08.458981
A1 Nicolas Decat
A1 Jasmine Walter
A1 Zhao H. Koh
A1 Piengkwan Sribanditmongkol
A1 Ben D. Fulcher
A1 Jennifer M. Windt
A1 Thomas Andrillon
A1 Naotsugu Tsuchiya
YR 2021
UL http://biorxiv.org/content/early/2021/09/09/2021.09.08.458981.abstract
AB Sleep is classically measured with electrophysiological recordings, which are then scored based on guidelines tailored for the visual inspection of these recordings. As such, these rules reflect a limited range of features easily captured by the human eye and do not always reflect the physiological changes associated with sleep. Here we present a novel analysis framework that characterizes sleep using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their features, without relying on established scoring conventions. The resulting structure overlapped substantially with that defined by visual scoring and we report novel features that are highly discriminative of sleep stages. However, we also observed discrepancies as hctsa features unraveled distinctive properties within traditional sleep stages. Our framework lays the groundwork for a data-driven exploration of sleep and the identification of new signatures of sleep disorders and conscious sleep states.Competing Interest StatementThe authors have declared no competing interest.